Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements

This paper is concerned with the receding horizon filtering problem for a class of discrete time-varying nonlinear systems with multiple missing measurements. The phenomenon of missing measurements occurs in a random way and the missing probability is governed by a set of stochastic variables obeying the given Bernoulli distribution. By exploiting the projection theory combined with stochastic analysis techniques, a Kalman-type receding horizon filter is put forward to facilitate the online applications. Furthermore, by utilizing the conditional expectation, a novel estimation scheme of state covariance matrices is proposed to guarantee the implementation of the filtering algorithm. Finally, a simulation example is provided to illustrate the effectiveness of the established filtering scheme.This work was supported in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University in Saudi Arabia [grant number 16-135-35-HiCi], the National Natural Science Foundation of China [grant number 61329301], [grant number 61203139], [grant number 61134009], and [grant number 61104125], Royal Society of the U.K., the Shanghai Rising-Star Program of China [grant number 13QA1400100], the Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation [grant number 13SG34], the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany

Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey

summary:Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research

Event-triggered distributed H∞ state estimation with packet dropouts through sensor networks

This study is concerned with the event-triggered distributed H∞ state estimation problem for a class of discrete-time stochastic non-linear systems with packet dropouts in a sensor network. An event-triggered communication mechanism is adopted over the sensor network with hope to reduce the communication burden and the energy consumption, where the measurements on each sensor are transmitted only when a certain triggering condition is violated. Furthermore, a novel distributed state estimator is designed where the available innovations are not only from the individual sensor, but also from its neighbouring ones according to the given topology. The purpose of the problem under consideration is to design a set of distributed state estimators such that the dynamics of estimation errors is exponentially mean-square stable and also the prespecified H∞ disturbance rejection attenuation level is guaranteed. By utilising the property of the Kronecker product and the stochastic analysis approaches, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimisation one that can be easily solved via available software packages. Finally, a simulation example is utilised to illustrate the usefulness of the proposed design scheme of event-triggered distributed state estimators.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139, 61473076, 61374127 and 61422301, the Shanghai Rising-Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany

Event-triggered consensus control for discrete-time stochastic multi-agent systems: The input-to-state stability in probability

This paper is concerned with the event-triggered consensus control problem for a class of discrete-time stochastic multi-agent systems with state-dependent noises. A novel definition of consensus in probability is proposed to better describe the dynamics of the consensus process of the addressed stochastic multiagent systems. The measurement output available for the controller is not only from the individual agent but also from its neighboring ones according to the given topology. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the control input on each agent is updated only when a certain triggering condition is violated. The purpose of the problem under consideration is to design both the output feedback controller and the threshold of the triggering condition such that the closed-loop system achieves the desired consensus in probability. First of all, a theoretical framework is established for analyzing the so-called input-to-state stability in probability (ISSiP) for general discretetime nonlinear stochastic systems. Within such a theoretical framework, some sufficient conditions on event-triggered control protocol are derived under which the consensus in probability is reached. Furthermore, both the controller parameter and the triggering threshold are obtained in terms of the solution to certain matrix inequalities involving the topology information and the desired consensus probability. Finally, a simulation example is utilized to illustrate the usefulness of the proposed control protocol.Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139 and 61473076, the Hujiang Foundation of China under Grants C14002 and D15009, the Shanghai Rising- Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of German

Event-based security control for discrete-time stochastic systems

This study is concerned with the event-based security control problem for a class of discrete-time stochastic systems with multiplicative noises subject to both randomly occurring denial-of-service (DoS) attacks and randomly occurring deception attacks. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the measurement signal is transmitted only when a certain triggering condition is violated. A novel attack model is proposed to reflect the randomly occurring behaviours of the DoS attacks as well as the deception attacks within a unified framework via two sets of Bernoulli distributed white sequences with known conditional probabilities. A new concept of mean-square security domain is put forward to quantify the security degree. The authors aim to design an output feedback controller such that the closed-loop system achieves the desired security. By using the stochastic analysis techniques, some sufficient conditions are established to guarantee the desired security requirement and the control gain is obtained by solving some linear matrix inequalities with nonlinear constraints. A simulation example is utilised to illustrate the usefulness of the proposed controller design scheme.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61573246 and 61374039, the Shanghai Rising-Star Programme of China under Grant 16QA1403000, the Program for Capability Construction of Shanghai Provincial Universities under Grant 15550502500 and the Alexander von Humboldt Foundation of Germany

An improved GMS-PROSAC algorithm for image mismatch elimination

Image matching usually plays a critical role for visual simultaneous localization and mapping (VSLAM). However, the resultant mismatches and low calculation efficiency of current matching algorithms reduces the performance of VSLAM. In order to overcome above two drawbacks, an improved GMS-PROSAC algorithm for image mismatch elimination is developed in this paper by introducing a new epipolar geometric constraint (EGC) model with a projection error function. This improved algorithm, named as GMS-EGCPROSAC, is made up of the traditional GMS algorithm and the improved PROSAC algorithm. First, the GMS algorithm is employed to obtain a rough matching set and then all matching pairs in this set are sorted according to their similarity degree. By selecting some smatching pairs with the highest similarity degree, the parameter of the EGC model is obtained. By resort to the calculated parameter, the improved PROSAC algorithm can be carried out to eliminate false matches. Finally, the real-time and the effectiveness are adequately verified by executing some contrast experiments. Our approach can not only quickly eliminate mismatches but also get more high-quality matching pairs